Heat-Traced Pipelines: A Double-Containment Solution for a High-Temperature Pipeline
Bibliographic record
Abstract
A pipe-in-pipe (PiP) technology was selected to transport hot fluids through muskeg and multiple obstacles dealt with Horizontal Directional Drillings (HDDs) in Northern Alberta. The system offers both integrity and construction advantages. • The outer pipe provides an obvious secondary barrier to any leak. It also provides the basis for a surveillance system with a sensitivity more than 1000 times the sensitivity provided by the standard mass-balance. • The system is pre-constrained: the inner pipe is pre-heated using electrical heat tracing at a temperature such that the system, once installed, has thermal stresses reduced by half and thus permits the installation of the high-temperature pipeline in non-competent soils with no expansion loop and no external anchor. • The insulation system between the two pipes reduces the heat loss to only 0.3 W/(m2.K), leading to a very long cool down time. Finally, the pipe-in-pipe can be a simple solution to high-risk and/or high scrutiny areas such as HDDs, sensitive wetlands, unstable soils, etc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".